2.1. Generalized Linear Models

Diagnostics for each model can be found here (presence/absence) and here (density).

2.2. Hierarchical Models of Species Communities

This section uses methodology and tools from Ovaskainen et al., with the direct help of Guillaume Blanchet.

First, we will compute models using the 108 stations with abiotic variables or exposure indices as predictors. 85 % of the stations (92) will act as training data, and the rest (16) will be used to validate the outputs. Second, these models will be used to predict taxa richness and distribution in the entire study area using predictor rasters.

We initiate the HMSC model with the chosen data:

• presence/absence or density for dependant variable
• exposure indices and/or abiotic variables for predictors

Priors and model parameters are set in the hmsc() function.

HMSC_PA <- hmsc(data, param, prior, family = "probit", niter = 100000, nburn = 1000, thin = 100)
HMSC_density <- hmsc(data, param, prior, family = "overPoisson", niter = 100000, nburn = 1000, thin = 100)

Here are the outcomes and diagnostics to evaluate each model’s quality (presented for each species seperately or averaged).

Diagnostics for each taxon can be found here (presence/absence) and here (density).

3.1. Generalized Linear Models

3.2. Hierarchical Modelling of Species Communities